Taipei, T'ai-pei, TW
1 day ago
Data Scientist, Enterprise Operations

Role Summary

The Data Scientist in Supply Chain Planning team is responsible for hands-on development of scenario models, simulations, optimization routines, and decision logic. This role operationalizes the Principal’s architecture into scalable tools and automated pipelines. Collaborates deeply with planners and finance leads.

Key Responsibilities

Develop and maintain scenario simulation modules (Monte Carlo, probabilistic lead time, demand shocks, capacity constraints).

Implement optimization models (e.g., ideal SL curves, cost-to-serve models, constrained supply allocations).

Build interpretable scenario outputs: forecast cones, shock sensitivity charts, risk heatmaps, buffer recommendations.

Own data pipelines and modeling layers supporting scenario execution.

Lead cross-regional analyses: risk exposure, scenario stress testing, velocity shifts, cannibalization, aging demand.

Translate business questions into structured scenario experiments.

Validate model accuracy, explainability, and alignment to enterprise planning logic.

Mentor Specialist-level DS and partner with planners on scenario consumption.

Drive continuous improvement—instrumentation, automation, reliability.

Required Skills & Experience

10+ years in Data Science, Modeling, Optimization, or Simulation.

Strong Python modeling capability (NumPy, Pandas, statsmodels/Prophet/PyTorch optional).

Hands-on experience with simulation (stochastic/empirical), discrete-event modeling, or time-series stress testing.

Working knowledge of optimization solvers (e.g., Gurobi, OR-Tools, Pyomo).

Ability to translate scenario outputs into decision-ready actions for supply/demand/finance teams.

Solid understanding of IBP/S&OP workflows, planning constraints, and parameter logic (MOQ, SS, ROP, capacity).

Ability to communicate complex models simply and effectively.

Preferred Experience

Experience connecting scenario models into planning systems (IBP, APS, SAP, custom Python).

Familiarity with LLM-assisted analysis and agent-driven simulation orchestration.

Prior leadership of scenario war rooms or supply chain risk reviews.

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